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I performed meta-regression in R and got the following results:

Mixed-Effects Model (k = 21; tau^2 estimator: ML)

tau^2 (estimated amount of residual heterogeneity):     0.1994 (SE = 0.0706)
tau (square root of estimated tau^2 value):             0.4466
I^2 (residual heterogeneity / unaccounted variability): 92.23%
H^2 (unaccounted variability / sampling variability):   12.88
R^2 (amount of heterogeneity accounted for):            7.18%

Test for Residual Heterogeneity:
QE(df = 19) = 465.3482, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 19) = 1.5851, p-val = 0.2233

Model Results:

              estimate      se     tval    pval    ci.lb   ci.ub 
intrcpt         0.9233  0.1866   4.9495  <.0001   0.5329  1.3138  *** 
participants   -0.1566  0.1244  -1.2590  0.2233  -0.4168  0.1037      

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

My questions is that for the participants group I have divided them into 1 (younger participants) and 2 (older participants), why can't I see the breakdown of the different participants here?

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    $\begingroup$ What does "estimate of participants = -0.1566" mean? $\endgroup$ – user158565 Jul 5 at 15:47
  • $\begingroup$ @user158565 perhaps you should have preceded your comment with Hint, hint, ... $\endgroup$ – mdewey Jul 5 at 16:27
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This is not really about meta-regression itself but about the meaning of regression coefficients. You have declared participants as a continuous variable so the coefficient labelled intrcpt (0.9233) is the estimated value for participants with score zero. If you add the coefficient for participants (-0.1566) you get the estimated values for participants with score one, and if you add twice the values (2 * -0.1566) you get the estimated value for participants with score two. Life would have been easier if you had either declared participants as a factor or had scored it 0/1.

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